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We are seeking a Research Assistant/Associate in Fluid Mechanics to join a cutting-edge project investigating perturbation dynamics and input–output behaviour in turbulent flows. Working
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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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://www.ntnu.edu/iv/doctoral-programme Strong background in fundamental or applied fluid mechanics. Programming skills in MATLAB, C, Python, or similar packages. Excellent command of the English language, and
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substantial background in fluid mechanics. Essential skills: Strong knowledge of numerical methods Ability to work effectively in a team Desirable skills / experience: Experience of applying CFD to a complex
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related discipline with substantial background in fluid mechanics. Essential skills: Strong knowledge of numerical methods Ability to work effectively in a team Desirable skills / experience: Experience
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to uncover the governing physical mechanisms and establish design principles for next-generation porous materials. A key application focus of the PhD is spontaneous fluid separation, especially for mixtures
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Application deadline: All year round Research theme: Computational Mechanics/Applied Mathematics How to apply: uom.link/pgr-apply-2425 This 3.5-year PhD project is fully funded and home students
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University of Technology (QUT, Brisbane, Australia) related to machine learning for particle laden fluid mechanics. QUT is a major Australian university with a global outlook and a 'real world' focus. We
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with a track record of research in fluid mechanics, boundary-layer theory & engineered surfaces. Collaboration: You will be co-supervised by Tim Reis, an expert in LBM with links to the lattice
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multiphysics models to investigate aquifer-based compressed air energy storage (CAES) systems. The research will involve coupling fluid flow, heat transfer, geomechanics, and potentially reactive transport